Text-to-image synthesis is one of the tasks within conditional image synthesis. With the advent of GANs, conditional GANs have achieved excellent performance on this task. Conditional GAN [1]} first proposed the conditional version of GAN which directly concatenates noise vector and conditional feature vector. However, text-to-image models based on this work fuse text information roughly by concatenating the text feature with noise vector. Vincent \(et\ al\)  [2]} proposed a more advanced fusion method (i.e., CIN) which uses adaptive mean and variance to control the image style. CIN and its variant are commonly used in recent works. For example, BigGAN [3]} and Style-GAN [4]} successively use CBN and CIN to achieve impressing visual quality on ImageNet.
